local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.GraphLayer')

function LSTMLayer:__init(id, global_conf, layer_conf)
    nerv.Layer.__init(self, id, global_conf, layer_conf)
    self:check_dim_len(-1, 1)
    if #self.dim_in == 0 then
        nerv.error('LSTM layer %s has no input', self.id)
    end

    local din = layer_conf.dim_in
    local dout = layer_conf.dim_out[1]

    local pr = layer_conf.pr
    if pr == nil then
        pr = nerv.ParamRepo({}, self.loc_type)
    end

    local layers = {
        ['nerv.CombinerLayer'] = {
            mainCombine = {dim_in = {dout, dout}, dim_out = {dout}, lambda = {1, 1}},
        },
        ['nerv.DuplicateLayer'] = {
            outputDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
            cellDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
        },
        ['nerv.AffineLayer'] = {
            mainAffine = {dim_in = table.connect(din, {dout}), dim_out = {dout}, pr = pr},
            forgetGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
            inputGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
            outputGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
        },
        ['nerv.TanhLayer'] = {
            mainTanh = {dim_in = {dout}, dim_out = {dout}},
            outputTanh = {dim_in = {dout}, dim_out = {dout}},
        },
        ['nerv.ElemMulLayer'] = {
            inputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
            forgetGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
            outputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
        },
    }
    for i = 1, #din do
        layers['nerv.DuplicateLayer']['inputDup' .. i] = {dim_in = {din[i]}, dim_out = {din[i], din[i], din[i], din[i]}}
    end
    
    local connections = {
        -- lstm input
        --{'<input>[1 .. n]', 'inputDup(1 .. n)[1]', 0},

        -- input gate
        {'outputDup[1]', 'inputGate[' .. (#din + 1) .. ']', 1},
        {'cellDup[1]', 'inputGate[' .. (#din + 2) .. ']', 1},
        --{'inputDup(1 .. n)[1]', 'inputGate[1 .. n]', 0},

        -- forget gate
        {'outputDup[2]', 'forgetGate[' .. (#din + 1) .. ']', 1},
        {'cellDup[2]', 'forgetGate[' .. (#din + 2) .. ']', 1},
        --{'inputDup(1 .. n)[2]', 'forgetGate[1 .. n]', 0},

        -- lstm cell
        {'forgetGate[1]', 'forgetGateMul[1]', 0},
        {'cellDup[3]', 'forgetGateMul[2]', 1},
        {'outputDup[3]', 'mainAffine[' .. (#din + 1) .. ']', 1},
        --{'inputDup(1 .. n)[3]', 'mainAffine[1 .. n]', 0},
        {'mainAffine[1]', 'mainTanh[1]', 0},
        {'inputGate[1]', 'inputGateMul[1]', 0},
        {'mainTanh[1]', 'inputGateMul[2]', 0},
        {'inputGateMul[1]', 'mainCombine[1]', 0},
        {'forgetGateMul[1]', 'mainCombine[2]', 0},
        {'mainCombine[1]', 'cellDup[1]', 0},

        -- forget gate
        {'outputDup[4]', 'outputGate[' .. (#din + 1) .. ']', 1},
        {'cellDup[4]', 'outputGate[' .. (#din + 2) .. ']', 0},
        --{'inputDup(1 .. n)[4]', 'outputGate[1 .. n]', 0},

        -- lstm output
        {'cellDup[5]', 'outputTanh[1]', 0},
        {'outputGate[1]', 'outputGateMul[1]', 0},
        {'outputTanh[1]', 'outputGateMul[2]', 0},
        {'outputGateMul[1]', 'outputDup[1]', 0},
        {'outputDup[5]', '<output>[1]', 0},
    }
    for i = 1, #din do
        table.insert(connections, {'<input>[' .. i .. ']', 'inputDup' .. i .. '[1]', 0})
        table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. i .. ']', 0})
        table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. i .. ']', 0})
        table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. i .. ']', 0})
        table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. i .. ']', 0})
    end

    self:add_prefix(layers, connections)
    local layer_repo = nerv.LayerRepo(layers, pr, global_conf)
    self.lrepo = layer_repo
    self:graph_init(layer_repo, connections)
end